//-------------------------------------------------------------------- // Simulation of a single GDP //--------------------------------------------------------------------- // ------------Definition of Parameters ------------------------------- // -------------------------------------------------------------------- // Determining Cl- Properties -------------------------------------- Cl_Steps = 11 // Number of Different [Cl-]i Min_Cl = 10 Max_Cl = 50 // Write Vektor with Cl- Values objref Cl_List Cl_List = new Vector(Cl_Steps) for i=0, Cl_Steps-1 { Cl_List.x[i] = Min_Cl + i*((Max_Cl-Min_Cl)/(Cl_Steps-1)) } // Determination Parameters GABA ------------------------------------ G_GABA = 0.000789 // synaptic weight according to miniature events DECAY_GABA = 37 P_GABA = 0 ngabasyn = 301 //Number of GABA-Synapses // calculated was 101 gninputs = 1 // number of inputs per synapse // Definition of various runtime parameters -------------------------- lenghtoutputvec = 6000 // Number of Lines for output (< 32000 for Excel-Figures) ndend=56 // Number of dendrites seed = 8 // seed for random function tstop = 1500 // Duration v_init = -60 // Initial voltage dt = 0.01 // Step Interval in ms // ------------Procedures and Functions ------------------------------- // -------------------------------------------------------------------- // Function MakeShort ---------------------------------------// // Inputs: \$1 Objref to Inputvector // // \$2 Objref to Outoutvector // // lenoutvec desired lendth of Outputvector // // // // Reduce Inputvec to Outputvev by averaging n elements // // n (reducing factor) = floor(Inputvec.size() / lenoutvec) // // ----------------------------------------------------------// obfunc MakeShort() {local i, n n = int(\$o1.size()/\$3) \$o2.resize(\$3) for i=0, \$3-1 { \$o2.x[i] = \$o1.mean(i*n, (i+1)*n-1) } return \$o2 } // End of function // ---------Definition of objects ------------------------------------- // -------------------------------------------------------------------- // Objects for GABA Synapses --------------------------------------------------------- objref GABA_SYN_LOCATION GABA_SYN_LOCATION = new Random(seed) // Position of GABA synapse as random Object objref gabasyn[ngabasyn] // Definition of synapse objects // random function for localization of synapses objref rand_loc // random function for synapses parameters objref rand_gaba_t, rand_gaba_g // definition of Vectors for Gaba-Stimulation (t_vec = timestamps t_vecr = sorted timestamps, g_vec = rel conductance) objref gabastim[ngabasyn], gaba_t_vec[ngabasyn], gaba_t_vecr[ngabasyn], synpulsegaba[ngabasyn], gaba_g_vec[ngabasyn] // Define vectors to link modelled parameter output --------------------------------- objref timevec, voltvec, clivec[ndend], hco3ivec[ndend] // vectors linked to parameter-pointers objref clivec_aver, hco3ivec_aver // vectors for Averagees over all Dendrites objref shorttimevec, shortvoltvec, shortclivec, shorthco3ivec, spacevec // shorter Vectros for output // Matrix for output 0 = time, 1 = Voltage, 2 = average Cli , 3 to 3+ndend, Cli n Dend[i] objref Outmatrix // Define Name of Output-File strdef OutFileName // Define Output File objref OutFile // Generate vectors and matrices ------------------------------------- voltvec = new Vector() timevec = new Vector() clivec_aver = new Vector() hco3ivec_aver = new Vector() shortvoltvec = new Vector() shorttimevec = new Vector() shortclivec = new Vector() shorthco3ivec = new Vector() spacevec = new Vector() for i=0, ndend-1 { clivec[i] = new Vector() hco3ivec[i] = new Vector() } Outmatrix = new Matrix() // Start of Input generation ------------------------------------------- // initiation of Random functions rand_loc = new Random(seed) rand_gaba_t = new Random(seed) rand_gaba_g = new Random(seed) //Define properties of random Function rand_gaba_t.normal(600, 9000) rand_gaba_g.normal(1, 0.28) //rel variance of GABA according to results // generate Vectors --- (gniputs, aninputs defines number of inputs per synapse) ------ for i = 0, ngabasyn-1 { gaba_t_vec[i] = new Vector(gninputs) gaba_t_vecr[i] = new Vector(gninputs) gaba_g_vec[i] = new Vector(gninputs) } // Distribute GABA synapses ----------------------------------------------------------- for k=0, ngabasyn-1 { pos = rand_loc.uniform(0,ndend-1) pos2 = GABA_SYN_LOCATION.uniform (0.0001, 0.999) dend_0[pos]{ gabasyn[k] = new gaba(pos2) gabasyn[k].tau1 = 0.1 gabasyn[k].tau2 = DECAY_GABA gabasyn[k].P = P_GABA } } //-- Simulation starts here ----------------------------------------------------------------- //------------------------------------------------------------------------------------------- // Loop Variation of Cl- -------------------------------------------------- Cl_Step = 0 while (Cl_Step < Cl_Steps){ // 1. define Cl- concentration ---------------------------------------------- forsec all { cli0_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step] cli_Start_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step] cli_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step] } printf("301-Sequence %g of %g; [Cl-]i = %g, dt = %g, p-GABA = %g, ", (Cl_Step+1), (Cl_Steps), Cl_List.x[Cl_Step], dt, P_GABA) // 2. Generate timestamps/conductances for GABA synapses -------------------------------------- for f=0, ngabasyn-1 { for i=0, gninputs-1 { t = rand_gaba_t.repick() g = rand_gaba_g.repick() gaba_t_vec[f].x[i]=t gaba_g_vec[f].x[i]=g * G_GABA } } for f=0, ngabasyn-1 { gaba_t_vecr[f] = gaba_t_vec[f].sort() } // 3. generate Vecstim-vectrors from the sorted timestamp-vectors ------------------------------- for i=0, ngabasyn-1 { gabastim[i] = new VecStim() gabastim[i].play(gaba_t_vecr[i]) } // GABA stimulator // 4. Play the Vecstim objects to the synapses --------------------------------------------- for i=0, ngabasyn-1 { synpulsegaba[i] = new NetCon(gabastim[i], gabasyn[i], 0, 0, G_GABA) } // GABA NetCon // 5. Link Objects to Output-Vectors ----------------------------------- timevec.record(&t) // Time vector voltvec.record(&v(.5)) // Volt vector in soma for i=0, ndend-1 { clivec[i].record(&dend_0[i].cli(0.5)) hco3ivec[i].record(&dend_0[i].hco3i(0.5)) } // 6. Run Simulation -------------------------------------------------------- run() // 8. Put Data in Output Vector ------------------------------------------------------ MakeShort(timevec, shorttimevec, lenghtoutputvec) Outmatrix.resize(shorttimevec.size()+1, Cl_Steps*4+1) Outmatrix.setcol(0, shorttimevec) spacevec.resize(shorttimevec.size()+1) //spavevec caries information about the properties during a loop) spacevec.fill(0) spacevec.x[0] = Cl_List.x[Cl_Step] spacevec.x[1] = P_GABA spacevec.x[2] = 777 Outmatrix.setcol(Cl_Step*4+1, spacevec) MakeShort(voltvec, shortvoltvec, lenghtoutputvec) Outmatrix.setcol(Cl_Step*4+2, shortvoltvec) // Calculate average [Cli] in dendrites ------- clivec_aver.mul(0) // empty vector hco3ivec_aver.mul(0) for i=0, ndend-1 { clivec_aver.resize(clivec[i].size()) clivec_aver.add(clivec[i]) hco3ivec_aver.resize(hco3ivec[i].size()) hco3ivec_aver.add(hco3ivec[i]) } clivec_aver.div(ndend) printf(", max [Cl-]i %g, min [Cl-]i %g \n", clivec_aver.max, clivec_aver.min) hco3ivec_aver.div(ndend) MakeShort(clivec_aver, shortclivec, lenghtoutputvec) MakeShort(hco3ivec_aver, shorthco3ivec, lenghtoutputvec) Outmatrix.setcol(Cl_Step*4+3, shortclivec) Outmatrix.setcol(Cl_Step*4+4, shorthco3ivec) // Goto next Cl- Concentration Cl_Step+=1 } // End inner loop --------------------------------- // Save the Data -------------------------------------------------------------------- OutFile = new File() sprint(OutFileName, "Result_VDpas_pGABA0_301-nGABA_Var_Cl_Short.asc") OutFile.wopen(OutFileName) Outmatrix.fprint(OutFile, "\t%g") OutFile.close